title: EEG seizure detection and epilepsy diagnosis using a novel variation of Empirical Mode Decomposition.
abstract: Epileptic seizure detection and epilepsy diagnosis based on feature extraction and classification using electroencephalography (EEG) signals is an important area of research. In this paper, we present a simple and effective approach based on signal decomposition, using a novel variation of the Empirical Mode Decomposition called Empirical Mode Decomposition-Modified Peak Selection (EMD-MPS). EMD-MPS allows time-scale based de-trending of signals, allowing signals to be separated directly into a de-trended component, and a trend, according to a frequency separation criterion. Features are extracted from the decomposed components, and a simple classifier, namely the 1-NN classifier is used for three classification tasks. The technique is tested on a publicly available EEG database, and a classification accuracy of 99% for epilepsy diagnosis task, and 100% and 98.2% for two seizure detection tasks is obtained. These results are better than, or comparable to previous results using the same EEG database, but have been obtained with a simpler and computationally fast signal analysis and classification method.
authors.forename: M
authors.initials: M
authors.lastname: Kaleem
authors.forename: A
authors.initials: A
authors.lastname: Guergachi
authors.forename: S
authors.initials: S
authors.lastname: Krishnan
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted